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legal-document-summarization – AI Model by sindhu2003 | AlphaNeural AI
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legal-document-summarization
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peft
tensorboard
safetensors
llama
trl
sft
generated_from_trainer
summarization
en
NousResearch/Llama-2-7b-hf
adapter
us
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legal-document-summarization
This model is a fine-tuned version of
NousResearch/Llama-2-7b-hf
on an unknown dataset. It achieves the following results on the evaluation set:
Loss: 1.2231
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
learning_rate: 0.0001
train_batch_size: 4
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 4
total_train_batch_size: 16
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.05
num_epochs: 2
mixed_precision_training: Native AMP
Training results
Training Loss
Epoch
Step
Validation Loss
1.2845
0.4
8
1.2406
1.2368
0.8
16
1.2264
1.0085
1.2
24
1.2255
0.9203
1.6
32
1.2244
0.9852
2.0
40
1.2231
Framework versions
PEFT 0.12.0
Transformers 4.42.3
Pytorch 2.1.2
Datasets 2.20.0
Tokenizers 0.19.1
